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Vol.15, No.2, May 2026. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
AI-Driven Teaching and Learning in Vocational Education: Current Trends and Future Directions
Yuliansah Yuliansah, Mahendra Adhi Nugroho, Sutirman Sutirman
© 2026 Yuliansah Yuliansah, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 15, Issue 2, Pages 1665-1680, ISSN 2217-8309, DOI: 10.18421/TEM152-60, May 2026.
Received: 15 May 2025.
Abstract:
Numerous studies have examined the role of Artificial Intelligence (AI) in education. However, research focusing on AI implementation in vocational education, particularly in teaching and learning processes, remains limited. This study maps research trends in AI-driven vocational teaching and learning through a bibliometric review, addressing: (1) Publication growth and geographical distribution, (2) Collaborative authorship patterns, (3) Keyword co-occurrence, and (4) Emerging thematic trends. A systematic literature review was conducted using the Scopus database, covering publications from 2001 to 2025. Data were collected on March 5, 2025, and analyzed using VOSviewer and Biblioshiny. The findings show that research in this area began in 2001 and grew rapidly, reaching 51 publications by the end of 2024. China dominated both publication output and citation impact, while Krirk University emerged as the most productive institution. Collaboration networks were largely driven by researchers from China and Malaysia, with key authors such as Zhang Z, Shen T-C, Chou C-M, Wang D, Li J, and Wu J playing central roles. The most influential keywords included Artificial Intelligence, Vocational Education, Student, and Teaching. Future research opportunities remain open in automation-based learning design, AI-supported learning management systems and instructional media, and evaluation practices through automated assessment, feedback generation, and AI-based test item development. These findings highlight AI’s strategic role as adaptive pedagogical support in fostering student-centered and self-regulated learning in vocational education, while AI-driven automation may also reduce teachers’ workload. Future studies are encouraged to adopt mixed-method and cross-country approaches to advance AI integration in vocational contexts.
Keywords – Artificial Intelligence, teaching, learning, vocational education, bibliometric. |
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